IDEAS home Printed from https://ideas.repec.org/a/spr/comgts/v21y2024i1d10.1007_s10287-023-00483-x.html
   My bibliography  Save this article

A constrained swarm optimization algorithm for large-scale long-run investments using Sharpe ratio-based performance measures

Author

Listed:
  • Massimiliano Kaucic

    (University of Trieste)

  • Filippo Piccotto

    (University of Trieste)

  • Gabriele Sbaiz

    (University of Trieste)

Abstract

We study large-scale portfolio optimization problems in which the aim is to maximize a multi-moment performance measure extending the Sharpe ratio. More specifically, we consider the adjusted for skewness Sharpe ratio, which incorporates the third moment of the returns distribution, and the adjusted for skewness and kurtosis Sharpe ratio, which exploits in addition the fourth moment. Further, we account for two types of real-world trading constraints. On the one hand, we impose stock market restrictions through cardinality, buy-in thresholds, and budget constraints. On the other hand, a turnover threshold restricts the total allowed amount of trades in the rebalancing phases. To deal with these asset allocation models, we embed a novel hybrid constraint-handling procedure into an improved dynamic level-based learning swarm optimizer. A repair operator maps candidate solutions onto the set characterized by the first type of constraints. Then, an adaptive $$\ell _1$$ ℓ 1 -exact penalty function manages turnover violations. The focus of the paper is to highlight the importance of including higher-order moments in the performance measures for long-run investments, in particular when the market is turbulent. We carry out empirical tests on two worldwide sets of assets to illustrate the scalability and effectiveness of the proposed strategies, and to evaluate the performance of our investments compared to the strategy maximizing the Sharpe ratio.

Suggested Citation

  • Massimiliano Kaucic & Filippo Piccotto & Gabriele Sbaiz, 2024. "A constrained swarm optimization algorithm for large-scale long-run investments using Sharpe ratio-based performance measures," Computational Management Science, Springer, vol. 21(1), pages 1-29, June.
    • Handle: RePEc:spr:comgts:v:21:y:2024:i:1:d:10.1007_s10287-023-00483-x
    DOI: 10.1007/s10287-023-00483-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10287-023-00483-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10287-023-00483-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kimball, Miles S, 1990. "Precautionary Saving in the Small and in the Large," Econometrica, Econometric Society, vol. 58(1), pages 53-73, January.
    2. Hitaj, Asmerilda & Zambruno, Giovanni, 2016. "Are Smart Beta strategies suitable for hedge fund portfolios?," Review of Financial Economics, Elsevier, vol. 29(C), pages 37-51.
    3. Guastaroba, Gianfranco & Mansini, Renata & Speranza, M. Grazia, 2009. "On the effectiveness of scenario generation techniques in single-period portfolio optimization," European Journal of Operational Research, Elsevier, vol. 192(2), pages 500-511, January.
    4. Alexei Chekhlov & Stanislav Uryasev & Michael Zabarankin, 2005. "Drawdown Measure In Portfolio Optimization," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 8(01), pages 13-58.
    5. Fred D. Arditti, 1967. "Risk And The Required Return On Equity," Journal of Finance, American Finance Association, vol. 22(1), pages 19-36, March.
    6. Charles-Olivier Amédée-Manesme & Fabrice Barthélémy, 2022. "Proper use of the modified Sharpe ratios in performance measurement: rearranging the Cornish Fisher expansion," Annals of Operations Research, Springer, vol. 313(2), pages 691-712, June.
    7. Scott, Robert C & Horvath, Philip A, 1980. "On the Direction of Preference for Moments of Higher Order Than the Variance," Journal of Finance, American Finance Association, vol. 35(4), pages 915-919, September.
    8. Paul A. Samuelson, 1970. "The Fundamental Approximation Theorem of Portfolio Analysis in terms of Means, Variances and Higher Moments," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 37(4), pages 537-542.
    9. Dimitris Politis & Halbert White, 2004. "Automatic Block-Length Selection for the Dependent Bootstrap," Econometric Reviews, Taylor & Francis Journals, vol. 23(1), pages 53-70.
    10. Colasante, Annarita & Riccetti, Luca, 2020. "Risk aversion, prudence and temperance: It is a matter of gap between moments," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    11. Marco Corazza & Giacomo di Tollo & Giovanni Fasano & Raffaele Pesenti, 2021. "A novel hybrid PSO-based metaheuristic for costly portfolio selection problems," Annals of Operations Research, Springer, vol. 304(1), pages 109-137, September.
    12. Patrizia Beraldi & Antonio Violi & Massimiliano Ferrara & Claudio Ciancio & Bruno Antonio Pansera, 2021. "Dealing with complex transaction costs in portfolio management," Annals of Operations Research, Springer, vol. 299(1), pages 7-22, April.
    13. Kim, Woo Chang & Fabozzi, Frank J. & Cheridito, Patrick & Fox, Charles, 2014. "Controlling portfolio skewness and kurtosis without directly optimizing third and fourth moments," Economics Letters, Elsevier, vol. 122(2), pages 154-158.
    14. Colasante, Annarita & Riccetti, Luca, 2021. "Financial and non-financial risk attitudes: What does it matter?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Emmanuel Jurczenko & Bertrand Maillet & Paul Merlin, 2008. "Efficient Frontier for Robust Higher-order Moment Portfolio Selection," Post-Print halshs-00336475, HAL.
    2. Lambert, M. & Hübner, G., 2013. "Comoment risk and stock returns," Journal of Empirical Finance, Elsevier, vol. 23(C), pages 191-205.
    3. Colasante, Annarita & Riccetti, Luca, 2020. "Risk aversion, prudence and temperance: It is a matter of gap between moments," Journal of Behavioral and Experimental Finance, Elsevier, vol. 25(C).
    4. Rand Kwong Yew Low, 2018. "Vine copulas: modelling systemic risk and enhancing higher‐moment portfolio optimisation," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 423-463, November.
    5. Briec, Walter & Kerstens, Kristiaan, 2010. "Portfolio selection in multidimensional general and partial moment space," Journal of Economic Dynamics and Control, Elsevier, vol. 34(4), pages 636-656, April.
    6. Monica Billio & Bertrand Maillet & Loriana Pelizzon, 2022. "A meta-measure of performance related to both investors and investments characteristics," Annals of Operations Research, Springer, vol. 313(2), pages 1405-1447, June.
    7. Annaert, Jan & De Ceuster, Marc & Van Cappellen, Jef, 2023. "Can average skewness really predict financial returns? The euro area case," Finance Research Letters, Elsevier, vol. 52(C).
    8. Walter Briec & Kristiaan Kerstens & Octave Jokung, 2007. "Mean-Variance-Skewness Portfolio Performance Gauging: A General Shortage Function and Dual Approach," Management Science, INFORMS, vol. 53(1), pages 135-149, January.
    9. Paul Karehnke & Frans de Roon, 2020. "Spanning Tests for Assets with Option-Like Payoffs: The Case of Hedge Funds," Management Science, INFORMS, vol. 66(12), pages 5969-5989, December.
    10. Eric Jondeau & Michael Rockinger, 2006. "Optimal Portfolio Allocation under Higher Moments," European Financial Management, European Financial Management Association, vol. 12(1), pages 29-55, January.
    11. Andriy Andreev & Antti Kanto & Pekka Malo, 2007. "Computational Examples of a New Method for Distribution Selection in the Pearson System," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(4), pages 487-506.
    12. Eric Jondeau & Michael Rockinger, 2005. "Conditional Asset Allocation under Non-Normality: How Costly is the Mean-Variance Criterion?," FAME Research Paper Series rp132, International Center for Financial Asset Management and Engineering.
    13. Yigit Atilgan & K. Ozgur Demirtas & A. Doruk Gunaydin & Imra Kirli, 2023. "Average skewness in global equity markets," International Review of Finance, International Review of Finance Ltd., vol. 23(2), pages 245-271, June.
    14. Salo, Ahti & Doumpos, Michalis & Liesiö, Juuso & Zopounidis, Constantin, 2024. "Fifty years of portfolio optimization," European Journal of Operational Research, Elsevier, vol. 318(1), pages 1-18.
    15. Krüger, Jens J., 2021. "Nonparametric portfolio efficiency measurement with higher moments," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 130825, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    16. Chabi-Yo, Fousseni & Leisen, Dietmar P.J. & Renault, Eric, 2014. "Aggregation of preferences for skewed asset returns," Journal of Economic Theory, Elsevier, vol. 154(C), pages 453-489.
    17. Fousseni Chabi-Yo, 2012. "Pricing Kernels with Stochastic Skewness and Volatility Risk," Management Science, INFORMS, vol. 58(3), pages 624-640, March.
    18. Bjorn Wahlroos & Tom Berglund, 1984. "Anomalies and Equilibrium Returns in a Small Stock Market," Discussion Papers 589, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
    19. Colasante, Annarita & Riccetti, Luca, 2021. "Financial and non-financial risk attitudes: What does it matter?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    20. Dai, Yingtong & Harris, Richard D.F., 2023. "Average tail risk and aggregate stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 82(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:comgts:v:21:y:2024:i:1:d:10.1007_s10287-023-00483-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.